电子学报2018，Vol.46Issue(1)：98-103,6.DOI:10.3969/j.issn.0372-2112.2018.01.014
基于稀疏贝叶斯的流形学习
Manifold Learning Based on Sparse Bayesian Approach
摘要
Abstract
Aiming at the classification performance deficiencies of current supervised learning algorithms on manifold data sets,e.g.low classification accuracy and limited sparsity,a sparse manifold learning algorithm based on sparse Bayesian inference and manifold regularization framework is proposed.The algorithm is called manifold learning based on sparse Bayesian approach (MLSBA).MLSBA is an extension of sparse Bayesian model,by introducing sparse manifold priors to the weights,which can effectively employ the manifold information of sample data to improve the classification accuracy.Extensive experiments are conducted on various datasets,and the results show that MLSBA not only achieves better classification performance on manifold datasets,but also has comparable effectiveness on the non-manifold datasets,and our algorithm has good sparsity on two categories of datasets at the same time.关键词
拉普拉斯/稀疏贝叶斯/稀疏流形先验/流形正则化Key words
Laplacian/sparse Bayesian/sparse manifold prior/manifold regularization分类
信息技术与安全科学引用本文复制引用
陈兵飞,江兵兵,周熙人,陈欢欢..基于稀疏贝叶斯的流形学习[J].电子学报,2018,46(1):98-103,6.基金项目
国家自然科学基金(No.91546116,No.61673363,No.61511130083) （No.91546116,No.61673363,No.61511130083）
